{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation.\n",
      "WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation.\n",
      "WARNING:root:Limited tf.summary API due to missing TensorBoard installation.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "import scipy\n",
    "from scipy.sparse import csr_matrix, lil_matrix, hstack, vstack\n",
    "import scipy.sparse as sprs\n",
    "from tqdm.auto import tqdm\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.externals import joblib\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "import datetime\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import copy\n",
    "import tensorflow as tf\n",
    "from keras.layers import Dense, Activation, Dropout\n",
    "from keras.models import Sequential\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.models import load_model\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import gc\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "from sklearn.utils.multiclass import unique_labels\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_download(features): #DOWNLOAD DATA DEPENDING ON A GIVEN LIST OF FEATURES TO USE\n",
    "    X_sub = []\n",
    "    if 'subscriptions' in features:\n",
    "        X_sub.append(sprs.load_npz('encoded_data/X_sub_full.npz'))\n",
    "        X_sub.append(sprs.load_npz('encoded_data/X_sub_trunc.npz'))\n",
    "    X_dem = []\n",
    "    if 'political' in features:\n",
    "        X_dem.append(sprs.load_npz('encoded_data/X_political.npz'))\n",
    "    if 'age' in features:\n",
    "        X_dem.append(sprs.load_npz('encoded_data/X_age.npz'))\n",
    "    if 'sex' in features:\n",
    "        X_dem.append(sprs.load_npz('encoded_data/X_sex.npz'))\n",
    "    if X_dem:\n",
    "        X_dem = scipy.sparse.hstack((X_dem), format='csr')\n",
    "    y = np.load('encoded_data/labels.npy')\n",
    "    return (X_sub, X_dem, y)\n",
    "\n",
    "def anomalies_seeking(sparse_matrix, bottom_threshold):  #FIND ANOMALIES: USERS, WHOSE NUMBER OF SUBSCRIPTIONS IS LESS or EQUAL THAN bottom_threshold  \n",
    "    \n",
    "    row_sums = sparse_matrix.sum(axis=1)\n",
    "    row_sums = np.array(row_sums).ravel()\n",
    "    id_to_delete = np.arange(sparse_matrix.shape[0])[row_sums <= bottom_threshold]\n",
    "    \n",
    "    return id_to_delete\n",
    "\n",
    "def delete_rows_csr(mat, indices):   #DELETE PARTICULAR ROWS IN CSR-MATRIX\n",
    "    \n",
    "    if not isinstance(mat, scipy.sparse.csr_matrix):\n",
    "        raise ValueError(\"works only for CSR format -- use .tocsr() first\")\n",
    "    \n",
    "    indices = list(indices)\n",
    "    mask = np.ones(mat.shape[0], dtype=bool)\n",
    "    mask[indices] = False\n",
    "    \n",
    "    return mat[mask]\n",
    "\n",
    "def anomalies_del(X_list, y, anomalies):  #DELETE PARTICULAR ROWS IN A GIVEN LIST OF FEATURE MATRICES AND THE CORRESPONDING VECTOR OF LABELS\n",
    "    \n",
    "    for i in range(len(X_list)):\n",
    "        X_list[i] = delete_rows_csr(X_list[i], anomalies)\n",
    "    \n",
    "    y = np.delete(y, anomalies, 0)\n",
    "    \n",
    "    return (X_list, y)\n",
    "\n",
    "def balancing_core(X, y): #BALANCE A FEATURE MATRIX AND THE CORRESPONDING VECTOR OF LABELS\n",
    "    sampler = RandomOverSampler(random_state=42)\n",
    "    X, y = sampler.fit_resample(X, y)\n",
    "    return (X, y)\n",
    "\n",
    "def plot_confusion_matrix(cm, classes): #PLOT A CONFUSION MATRIX\n",
    "    cmap=\"Greys\"\n",
    "    \n",
    "    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    \n",
    "    plt.figure(figsize=(4,4))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    #title='Confusion matrix'\n",
    "    #plt.title(title)\n",
    "    #plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    #plt.xticks(tick_marks, classes, rotation=45)\n",
    "    matplotlib.rcParams.update({'font.size': 10})\n",
    "    plt.xticks(tick_marks, classes)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    fmt = '.2f' \n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    plt.ylabel('Real Class')\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return cm\n",
    "\n",
    "def recall_av(cnf_matrix):  #COMPUTE AVERAGE RECALL\n",
    "    return cnf_matrix.trace()/cnf_matrix.shape[0]\n",
    "\n",
    "def prediction(clf, X, y, classes_names):  #EVALUATE PERFORMANCE OF A GIVEN MODEL WITH RESPECT TO A PARTICULAR DATA\n",
    "    y_predict = clf.predict(X)\n",
    "    \n",
    "    cnf_matrix = confusion_matrix(y, y_predict)\n",
    "    cm = plot_confusion_matrix(cnf_matrix, classes_names)\n",
    "    \n",
    "    acc = accuracy_score(y, y_predict)\n",
    "    print('Accuracy score:', acc)\n",
    "    return 0\n",
    "\n",
    "def prediction_Neuro(model, X, y, classes_names, labelsize):\n",
    "    y_predict = model.predict(X).argmax(axis=1)+1\n",
    "    #print(np.unique(np.array(y)), np.unique(np.array(y_predict)))\n",
    "    cnf_matrix = confusion_matrix(y, y_predict)\n",
    "    cm = plot_confusion_matrix(cnf_matrix, classes_names)\n",
    "    acc = accuracy_score(y, y_predict)\n",
    "    print('Accuracy score:', acc)\n",
    "    print('\\n')\n",
    "    return 0\n",
    "\n",
    "\n",
    "def model_fit_test(clf_type, features, bottom_threshold, Number_of_components, names):  #PERFORM ALL STEPS NEEDED TO FIT THE CLASSIFIER AND CHECK ITS PERFORMANCE     \n",
    "    \n",
    "    #DOWNLOAD DATA\n",
    "    condition1 = ('political' in features) or ('age' in features) or ('sex' in features) \n",
    "    condition2 = 'subscriptions' in features\n",
    "    \n",
    "    if condition2:\n",
    "        ([X_sub_full, X_sub_trunc], X_dem, y) = data_download(features)\n",
    "    else:\n",
    "        (X_sub, X_dem, y) = data_download(features)\n",
    "    \n",
    "    #FIND AND DELETE ANOMALIES\n",
    "    if condition1:\n",
    "        X_dem = X_dem.astype(int)\n",
    "    if condition2:\n",
    "        anomalies_id = anomalies_seeking(X_sub_full, bottom_threshold)  \n",
    "        X_sub_full = X_sub_full.astype(int)\n",
    "        X_sub_trunc = X_sub_trunc.astype(int)\n",
    "        y = y.astype(int)\n",
    "        if condition1:\n",
    "            ([X_sub_full, X_sub_trunc, X_dem], y) = anomalies_del([X_sub_full, X_sub_trunc, X_dem], y, anomalies_id)  \n",
    "        else:\n",
    "            ([X_sub_full, X_sub_trunc], y) = anomalies_del([X_sub_full, X_sub_trunc], y, anomalies_id)\n",
    "    \n",
    "    if condition1 and condition2:\n",
    "        X_full = scipy.sparse.hstack((X_sub_full, X_dem))\n",
    "        X_trunc = scipy.sparse.hstack((X_sub_trunc, X_dem))\n",
    "        del(X_sub_full, X_dem, X_sub_trunc)\n",
    "    elif not condition1 and condition2:\n",
    "        X_full = X_sub_full\n",
    "        X_trunc = X_sub_trunc\n",
    "        del(X_sub_full, X_sub_trunc)\n",
    "    elif condition1 and not condition2:\n",
    "        X_full = X_dem\n",
    "        X_trunc = X_dem\n",
    "        del(X_dem)\n",
    "    else:\n",
    "        print('No features were given')\n",
    "        return 0\n",
    "    \n",
    "    #BALANCE THE DATA\n",
    "    (X_full, y_full) = balancing_core(X_full, y)\n",
    "    (X_trunc, y_trunc) = balancing_core(X_trunc, y)\n",
    "    del(y)\n",
    "    \n",
    "    #CREATE TRAIN AND TEST SAMPLES FOR BOTH FULL AND TRUNCATED PIECES OF DATA\n",
    "    X_full_train, X_full_test, y_full_train, y_full_test = train_test_split(X_full, \n",
    "                                                                            y_full, \n",
    "                                                                            test_size=0.33, random_state=42)\n",
    "    X_trunc_train, X_trunc_test, y_trunc_train, y_trunc_test = train_test_split(X_trunc, \n",
    "                                                                                y_trunc, \n",
    "                                                                                test_size=0.33, random_state=42)\n",
    "    del(X_full_test, y_full_test)\n",
    "    X_train = scipy.sparse.vstack((X_full_train, X_trunc_train))\n",
    "    del(X_full_train, X_trunc_train)\n",
    "    X_test = X_trunc_test   \n",
    "    del(X_trunc_test)\n",
    "    y_train = np.hstack((y_full_train, y_trunc_train))\n",
    "    del(y_trunc_train)\n",
    "    y_test = y_trunc_test\n",
    "    del(y_trunc_test)\n",
    "    \n",
    "    X_train = X_train.astype(float)\n",
    "    X_test = X_test.astype(float)\n",
    "    y_train = y_train.astype(float)\n",
    "    y_test = y_test.astype(float)\n",
    "    \n",
    "    #APPLY SINGULAR VALUE DECOMPOSITION\n",
    "    if Number_of_components > 0:\n",
    "        if Number_of_components < X_train.shape[1]:\n",
    "        #Number_of_components = min(Number_of_components, X_train.shape[1]-1)\n",
    "            #CALL FOR A DECOMPOSER\n",
    "            svd = TruncatedSVD(algorithm='arpack', n_components=Number_of_components, random_state=42)\n",
    "            #FIT THE DECOMPOSER\n",
    "            svd.fit(X_train)\n",
    "            joblib.dump(svd, 'fitted_models' + '/' + 'decomposer_' + clf_type + '.save')\n",
    "            #APPLY THE FITTED DECOMPOSER\n",
    "            X_train = svd.transform(X_train)\n",
    "            X_test = svd.transform(X_test)\n",
    "        else:\n",
    "            pass\n",
    "    \n",
    "    #CALL FOR A CLASSIFIER\n",
    "    if clf_type == 'LG':\n",
    "        \n",
    "        clf = LogisticRegression(C=0.1, n_jobs=-1, random_state=42)  \n",
    "        \n",
    "    \n",
    "        #TRAIN CLASSIFIER\n",
    "        clf.fit(X_train, y_train)\n",
    "        joblib.dump(clf, 'fitted_models' + '/' + 'clf_' + clf_type + '.save') \n",
    "        #CHECK ITS PERFORMANCE RATES ON TRAIN AND TEST SAMPLES\n",
    "        print('Performance on the train sample:')\n",
    "        prediction(clf, X_train, y_train, names)\n",
    "        print('\\n')\n",
    "        print('Performance on the test sample:')\n",
    "        prediction(clf, X_test, y_test, names)\n",
    "    \n",
    "    elif clf_type == 'NN':\n",
    "        \n",
    "        epochs=100\n",
    "        layersNum=3\n",
    "        layersSize=[20, 10, 4]\n",
    "        batch_size=64\n",
    "        \n",
    "        #X_val = X_test\n",
    "        #y_val = copy.copy(y_test)\n",
    "\n",
    "        onehot_encoder = OneHotEncoder(sparse=False)\n",
    "        y_train_encoded = onehot_encoder.fit_transform(y_train.reshape(-1, 1))\n",
    "        y_test_encoded = onehot_encoder.transform(y_test.reshape(-1, 1))\n",
    "        #y_val = onehot_encoder.transform(y_val.reshape(-1, 1))\n",
    "\n",
    "        tempFolder = 'TempModelsSave'\n",
    "        filepath = 'clf_NN'\n",
    "        model = Sequential()\n",
    "        model.add(Dense(layersSize[0], input_dim=X_train.shape[1], activation='relu'))\n",
    "        for i in range(1, layersNum-1):\n",
    "            model.add(Dense(layersSize[i], activation='relu'))\n",
    "        model.add(Dense(len(names), activation='softmax'))\n",
    "        model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "        checkpoint = ModelCheckpoint(tempFolder + \"/\" + filepath + \".hdf5\", \n",
    "                                     monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')\n",
    "        #tempFolder + \"/\" + \n",
    "        model.fit(X_train, y_train_encoded, batch_size=batch_size, epochs=epochs, shuffle=True, \n",
    "                  callbacks=[checkpoint], \n",
    "                  validation_data=(X_test, y_test_encoded))\n",
    "\n",
    "        model.load_weights(tempFolder + \"/\" + filepath + \".hdf5\")\n",
    "        model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "        model.save(\"fitted_models/\" + filepath + \".h5\")\n",
    "        \n",
    "        print('Performance on the train sample:')\n",
    "        prediction_Neuro(model, X_train, y_train, names, 15)\n",
    "        print('Performance on the test sample:')\n",
    "        prediction_Neuro(model, X_test, y_test, names, 15)\n",
    "    \n",
    "    \n",
    "    #Add a classifier to try another predictive modelss\n",
    "    \n",
    "    return 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LG performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance on the train sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.7301658657019412\n",
      "\n",
      "\n",
      "Performance on the test sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.6138222276951606\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_threshold = 10\n",
    "features = ['political', 'subscriptions']\n",
    "Number_of_components = 100\n",
    "clf_type='LG'\n",
    "\n",
    "model_fit_test(clf_type, features, bottom_threshold, Number_of_components, ['1', '2', '3', '4'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NN performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 560574 samples, validate on 138053 samples\n",
      "Epoch 1/100\n",
      "560574/560574 [==============================] - 41s 72us/step - loss: 0.6894 - accuracy: 0.7254 - val_loss: 0.8985 - val_accuracy: 0.6249\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.62488, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 2/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6420 - accuracy: 0.7405 - val_loss: 0.8884 - val_accuracy: 0.6279\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.62488 to 0.62793, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 3/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6344 - accuracy: 0.7423 - val_loss: 0.8857 - val_accuracy: 0.6270\n",
      "\n",
      "Epoch 00003: val_accuracy did not improve from 0.62793\n",
      "Epoch 4/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6304 - accuracy: 0.7438 - val_loss: 0.8850 - val_accuracy: 0.6270\n",
      "\n",
      "Epoch 00004: val_accuracy did not improve from 0.62793\n",
      "Epoch 5/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6277 - accuracy: 0.7446 - val_loss: 0.8833 - val_accuracy: 0.6269\n",
      "\n",
      "Epoch 00005: val_accuracy did not improve from 0.62793\n",
      "Epoch 6/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6255 - accuracy: 0.7450 - val_loss: 0.8802 - val_accuracy: 0.6297\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.62793 to 0.62973, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 7/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6236 - accuracy: 0.7457 - val_loss: 0.8835 - val_accuracy: 0.6313\n",
      "\n",
      "Epoch 00007: val_accuracy improved from 0.62973 to 0.63134, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 8/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6223 - accuracy: 0.7459 - val_loss: 0.8796 - val_accuracy: 0.6312\n",
      "\n",
      "Epoch 00008: val_accuracy did not improve from 0.63134\n",
      "Epoch 9/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6212 - accuracy: 0.7463 - val_loss: 0.8794 - val_accuracy: 0.6298\n",
      "\n",
      "Epoch 00009: val_accuracy did not improve from 0.63134\n",
      "Epoch 10/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6201 - accuracy: 0.7466 - val_loss: 0.8765 - val_accuracy: 0.6313\n",
      "\n",
      "Epoch 00010: val_accuracy did not improve from 0.63134\n",
      "Epoch 11/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6193 - accuracy: 0.7469 - val_loss: 0.8762 - val_accuracy: 0.6313\n",
      "\n",
      "Epoch 00011: val_accuracy did not improve from 0.63134\n",
      "Epoch 12/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6182 - accuracy: 0.7469 - val_loss: 0.8799 - val_accuracy: 0.6319\n",
      "\n",
      "Epoch 00012: val_accuracy improved from 0.63134 to 0.63193, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 13/100\n",
      "560574/560574 [==============================] - 41s 72us/step - loss: 0.6176 - accuracy: 0.7474 - val_loss: 0.8757 - val_accuracy: 0.6328\n",
      "\n",
      "Epoch 00013: val_accuracy improved from 0.63193 to 0.63281, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 14/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6168 - accuracy: 0.7477 - val_loss: 0.8769 - val_accuracy: 0.6327\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.63281\n",
      "Epoch 15/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6164 - accuracy: 0.7484 - val_loss: 0.8761 - val_accuracy: 0.6325\n",
      "\n",
      "Epoch 00015: val_accuracy did not improve from 0.63281\n",
      "Epoch 16/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6160 - accuracy: 0.7478 - val_loss: 0.8764 - val_accuracy: 0.6331\n",
      "\n",
      "Epoch 00016: val_accuracy improved from 0.63281 to 0.63314, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 17/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6155 - accuracy: 0.7482 - val_loss: 0.8782 - val_accuracy: 0.6340\n",
      "\n",
      "Epoch 00017: val_accuracy improved from 0.63314 to 0.63400, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 18/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6151 - accuracy: 0.7479 - val_loss: 0.8740 - val_accuracy: 0.6334\n",
      "\n",
      "Epoch 00018: val_accuracy did not improve from 0.63400\n",
      "Epoch 19/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6147 - accuracy: 0.7485 - val_loss: 0.8739 - val_accuracy: 0.6349\n",
      "\n",
      "Epoch 00019: val_accuracy improved from 0.63400 to 0.63492, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 20/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6142 - accuracy: 0.7486 - val_loss: 0.8760 - val_accuracy: 0.6329\n",
      "\n",
      "Epoch 00020: val_accuracy did not improve from 0.63492\n",
      "Epoch 21/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6139 - accuracy: 0.7490 - val_loss: 0.8763 - val_accuracy: 0.6345\n",
      "\n",
      "Epoch 00021: val_accuracy did not improve from 0.63492\n",
      "Epoch 22/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6135 - accuracy: 0.7490 - val_loss: 0.8749 - val_accuracy: 0.6340\n",
      "\n",
      "Epoch 00022: val_accuracy did not improve from 0.63492\n",
      "Epoch 23/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6132 - accuracy: 0.7488 - val_loss: 0.8747 - val_accuracy: 0.6328\n",
      "\n",
      "Epoch 00023: val_accuracy did not improve from 0.63492\n",
      "Epoch 24/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6129 - accuracy: 0.7492 - val_loss: 0.8718 - val_accuracy: 0.6335\n",
      "\n",
      "Epoch 00024: val_accuracy did not improve from 0.63492\n",
      "Epoch 25/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6125 - accuracy: 0.7497 - val_loss: 0.8733 - val_accuracy: 0.6332\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.63492\n",
      "Epoch 26/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6123 - accuracy: 0.7492 - val_loss: 0.8741 - val_accuracy: 0.6343\n",
      "\n",
      "Epoch 00026: val_accuracy did not improve from 0.63492\n",
      "Epoch 27/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6120 - accuracy: 0.7494 - val_loss: 0.8724 - val_accuracy: 0.6341\n",
      "\n",
      "Epoch 00027: val_accuracy did not improve from 0.63492\n",
      "Epoch 28/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6119 - accuracy: 0.7493 - val_loss: 0.8714 - val_accuracy: 0.6337\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.63492\n",
      "Epoch 29/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6116 - accuracy: 0.7496 - val_loss: 0.8718 - val_accuracy: 0.6351\n",
      "\n",
      "Epoch 00029: val_accuracy improved from 0.63492 to 0.63508, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 30/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6113 - accuracy: 0.7496 - val_loss: 0.8731 - val_accuracy: 0.6330\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.63508\n",
      "Epoch 31/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6112 - accuracy: 0.7497 - val_loss: 0.8740 - val_accuracy: 0.6328\n",
      "\n",
      "Epoch 00031: val_accuracy did not improve from 0.63508\n",
      "Epoch 32/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6109 - accuracy: 0.7500 - val_loss: 0.8736 - val_accuracy: 0.6352\n",
      "\n",
      "Epoch 00032: val_accuracy improved from 0.63508 to 0.63521, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 33/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6108 - accuracy: 0.7502 - val_loss: 0.8710 - val_accuracy: 0.6362\n",
      "\n",
      "Epoch 00033: val_accuracy improved from 0.63521 to 0.63618, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 34/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6105 - accuracy: 0.7500 - val_loss: 0.8769 - val_accuracy: 0.6316\n",
      "\n",
      "Epoch 00034: val_accuracy did not improve from 0.63618\n",
      "Epoch 35/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6105 - accuracy: 0.7498 - val_loss: 0.8723 - val_accuracy: 0.6316\n",
      "\n",
      "Epoch 00035: val_accuracy did not improve from 0.63618\n",
      "Epoch 36/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6102 - accuracy: 0.7499 - val_loss: 0.8730 - val_accuracy: 0.6326\n",
      "\n",
      "Epoch 00036: val_accuracy did not improve from 0.63618\n",
      "Epoch 37/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6101 - accuracy: 0.7502 - val_loss: 0.8711 - val_accuracy: 0.6346\n",
      "\n",
      "Epoch 00037: val_accuracy did not improve from 0.63618\n",
      "Epoch 38/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6099 - accuracy: 0.7504 - val_loss: 0.8712 - val_accuracy: 0.6348\n",
      "\n",
      "Epoch 00038: val_accuracy did not improve from 0.63618\n",
      "Epoch 39/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6097 - accuracy: 0.7502 - val_loss: 0.8724 - val_accuracy: 0.6352\n",
      "\n",
      "Epoch 00039: val_accuracy did not improve from 0.63618\n",
      "Epoch 40/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6097 - accuracy: 0.7502 - val_loss: 0.8720 - val_accuracy: 0.6354\n",
      "\n",
      "Epoch 00040: val_accuracy did not improve from 0.63618\n",
      "Epoch 41/100\n",
      "560574/560574 [==============================] - 40s 70us/step - loss: 0.6094 - accuracy: 0.7503 - val_loss: 0.8715 - val_accuracy: 0.6345\n",
      "\n",
      "Epoch 00041: val_accuracy did not improve from 0.63618\n",
      "Epoch 42/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6092 - accuracy: 0.7506 - val_loss: 0.8764 - val_accuracy: 0.6340\n",
      "\n",
      "Epoch 00042: val_accuracy did not improve from 0.63618\n",
      "Epoch 43/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6092 - accuracy: 0.7504 - val_loss: 0.8711 - val_accuracy: 0.6372\n",
      "\n",
      "Epoch 00043: val_accuracy improved from 0.63618 to 0.63720, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 44/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6092 - accuracy: 0.7508 - val_loss: 0.8719 - val_accuracy: 0.6335\n",
      "\n",
      "Epoch 00044: val_accuracy did not improve from 0.63720\n",
      "Epoch 45/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6090 - accuracy: 0.7506 - val_loss: 0.8699 - val_accuracy: 0.6358\n",
      "\n",
      "Epoch 00045: val_accuracy did not improve from 0.63720\n",
      "Epoch 46/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6090 - accuracy: 0.7507 - val_loss: 0.8702 - val_accuracy: 0.6324\n",
      "\n",
      "Epoch 00046: val_accuracy did not improve from 0.63720\n",
      "Epoch 47/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6088 - accuracy: 0.7509 - val_loss: 0.8702 - val_accuracy: 0.6351\n",
      "\n",
      "Epoch 00047: val_accuracy did not improve from 0.63720\n",
      "Epoch 48/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6087 - accuracy: 0.7511 - val_loss: 0.8705 - val_accuracy: 0.6364\n",
      "\n",
      "Epoch 00048: val_accuracy did not improve from 0.63720\n",
      "Epoch 49/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6084 - accuracy: 0.7514 - val_loss: 0.8707 - val_accuracy: 0.6330\n",
      "\n",
      "Epoch 00049: val_accuracy did not improve from 0.63720\n",
      "Epoch 50/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6085 - accuracy: 0.7511 - val_loss: 0.8721 - val_accuracy: 0.6342\n",
      "\n",
      "Epoch 00050: val_accuracy did not improve from 0.63720\n",
      "Epoch 51/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6084 - accuracy: 0.7509 - val_loss: 0.8697 - val_accuracy: 0.6367\n",
      "\n",
      "Epoch 00051: val_accuracy did not improve from 0.63720\n",
      "Epoch 52/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6082 - accuracy: 0.7509 - val_loss: 0.8716 - val_accuracy: 0.6353\n",
      "\n",
      "Epoch 00052: val_accuracy did not improve from 0.63720\n",
      "Epoch 53/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6082 - accuracy: 0.7508 - val_loss: 0.8714 - val_accuracy: 0.6366\n",
      "\n",
      "Epoch 00053: val_accuracy did not improve from 0.63720\n",
      "Epoch 54/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6080 - accuracy: 0.7509 - val_loss: 0.8703 - val_accuracy: 0.6350\n",
      "\n",
      "Epoch 00054: val_accuracy did not improve from 0.63720\n",
      "Epoch 55/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6080 - accuracy: 0.7508 - val_loss: 0.8725 - val_accuracy: 0.6344\n",
      "\n",
      "Epoch 00055: val_accuracy did not improve from 0.63720\n",
      "Epoch 56/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6080 - accuracy: 0.7514 - val_loss: 0.8718 - val_accuracy: 0.6361\n",
      "\n",
      "Epoch 00056: val_accuracy did not improve from 0.63720\n",
      "Epoch 57/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6078 - accuracy: 0.7511 - val_loss: 0.8734 - val_accuracy: 0.6341\n",
      "\n",
      "Epoch 00057: val_accuracy did not improve from 0.63720\n",
      "Epoch 58/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6077 - accuracy: 0.7512 - val_loss: 0.8700 - val_accuracy: 0.6370\n",
      "\n",
      "Epoch 00058: val_accuracy did not improve from 0.63720\n",
      "Epoch 59/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6077 - accuracy: 0.7512 - val_loss: 0.8714 - val_accuracy: 0.6342\n",
      "\n",
      "Epoch 00059: val_accuracy did not improve from 0.63720\n",
      "Epoch 60/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6075 - accuracy: 0.7514 - val_loss: 0.8713 - val_accuracy: 0.6364\n",
      "\n",
      "Epoch 00060: val_accuracy did not improve from 0.63720\n",
      "Epoch 61/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6077 - accuracy: 0.7511 - val_loss: 0.8717 - val_accuracy: 0.6340\n",
      "\n",
      "Epoch 00061: val_accuracy did not improve from 0.63720\n",
      "Epoch 62/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6075 - accuracy: 0.7511 - val_loss: 0.8714 - val_accuracy: 0.6332\n",
      "\n",
      "Epoch 00062: val_accuracy did not improve from 0.63720\n",
      "Epoch 63/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6073 - accuracy: 0.7513 - val_loss: 0.8705 - val_accuracy: 0.6356\n",
      "\n",
      "Epoch 00063: val_accuracy did not improve from 0.63720\n",
      "Epoch 64/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6073 - accuracy: 0.7514 - val_loss: 0.8697 - val_accuracy: 0.6361\n",
      "\n",
      "Epoch 00064: val_accuracy did not improve from 0.63720\n",
      "Epoch 65/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6073 - accuracy: 0.7517 - val_loss: 0.8695 - val_accuracy: 0.6342\n",
      "\n",
      "Epoch 00065: val_accuracy did not improve from 0.63720\n",
      "Epoch 66/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6072 - accuracy: 0.7514 - val_loss: 0.8698 - val_accuracy: 0.6361\n",
      "\n",
      "Epoch 00066: val_accuracy did not improve from 0.63720\n",
      "Epoch 67/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6071 - accuracy: 0.7514 - val_loss: 0.8701 - val_accuracy: 0.6355\n",
      "\n",
      "Epoch 00067: val_accuracy did not improve from 0.63720\n",
      "Epoch 68/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6068 - accuracy: 0.7513 - val_loss: 0.8713 - val_accuracy: 0.6360\n",
      "\n",
      "Epoch 00068: val_accuracy did not improve from 0.63720\n",
      "Epoch 69/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6071 - accuracy: 0.7517 - val_loss: 0.8713 - val_accuracy: 0.6363\n",
      "\n",
      "Epoch 00069: val_accuracy did not improve from 0.63720\n",
      "Epoch 70/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6069 - accuracy: 0.7516 - val_loss: 0.8716 - val_accuracy: 0.6349\n",
      "\n",
      "Epoch 00070: val_accuracy did not improve from 0.63720\n",
      "Epoch 71/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6067 - accuracy: 0.7516 - val_loss: 0.8734 - val_accuracy: 0.6367\n",
      "\n",
      "Epoch 00071: val_accuracy did not improve from 0.63720\n",
      "Epoch 72/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6068 - accuracy: 0.7515 - val_loss: 0.8692 - val_accuracy: 0.6376\n",
      "\n",
      "Epoch 00072: val_accuracy improved from 0.63720 to 0.63759, saving model to TempModelsSave/clf_NN.hdf5\n",
      "Epoch 73/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6066 - accuracy: 0.7516 - val_loss: 0.8703 - val_accuracy: 0.6365\n",
      "\n",
      "Epoch 00073: val_accuracy did not improve from 0.63759\n",
      "Epoch 74/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6066 - accuracy: 0.7520 - val_loss: 0.8684 - val_accuracy: 0.6357\n",
      "\n",
      "Epoch 00074: val_accuracy did not improve from 0.63759\n",
      "Epoch 75/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6068 - accuracy: 0.7514 - val_loss: 0.8717 - val_accuracy: 0.6341\n",
      "\n",
      "Epoch 00075: val_accuracy did not improve from 0.63759\n",
      "Epoch 76/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6065 - accuracy: 0.7517 - val_loss: 0.8681 - val_accuracy: 0.6368\n",
      "\n",
      "Epoch 00076: val_accuracy did not improve from 0.63759\n",
      "Epoch 77/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6064 - accuracy: 0.7516 - val_loss: 0.8698 - val_accuracy: 0.6367\n",
      "\n",
      "Epoch 00077: val_accuracy did not improve from 0.63759\n",
      "Epoch 78/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6064 - accuracy: 0.7521 - val_loss: 0.8706 - val_accuracy: 0.6343\n",
      "\n",
      "Epoch 00078: val_accuracy did not improve from 0.63759\n",
      "Epoch 79/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6063 - accuracy: 0.7519 - val_loss: 0.8703 - val_accuracy: 0.6354\n",
      "\n",
      "Epoch 00079: val_accuracy did not improve from 0.63759\n",
      "Epoch 80/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6063 - accuracy: 0.7522 - val_loss: 0.8698 - val_accuracy: 0.6351\n",
      "\n",
      "Epoch 00080: val_accuracy did not improve from 0.63759\n",
      "Epoch 81/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6063 - accuracy: 0.7522 - val_loss: 0.8718 - val_accuracy: 0.6370\n",
      "\n",
      "Epoch 00081: val_accuracy did not improve from 0.63759\n",
      "Epoch 82/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6062 - accuracy: 0.7520 - val_loss: 0.8704 - val_accuracy: 0.6353\n",
      "\n",
      "Epoch 00082: val_accuracy did not improve from 0.63759\n",
      "Epoch 83/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6061 - accuracy: 0.7519 - val_loss: 0.8704 - val_accuracy: 0.6363\n",
      "\n",
      "Epoch 00083: val_accuracy did not improve from 0.63759\n",
      "Epoch 84/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6061 - accuracy: 0.7521 - val_loss: 0.8704 - val_accuracy: 0.6374\n",
      "\n",
      "Epoch 00084: val_accuracy did not improve from 0.63759\n",
      "Epoch 85/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6061 - accuracy: 0.7518 - val_loss: 0.8700 - val_accuracy: 0.6366\n",
      "\n",
      "Epoch 00085: val_accuracy did not improve from 0.63759\n",
      "Epoch 86/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6060 - accuracy: 0.7520 - val_loss: 0.8735 - val_accuracy: 0.6350\n",
      "\n",
      "Epoch 00086: val_accuracy did not improve from 0.63759\n",
      "Epoch 87/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6061 - accuracy: 0.7520 - val_loss: 0.8709 - val_accuracy: 0.6334\n",
      "\n",
      "Epoch 00087: val_accuracy did not improve from 0.63759\n",
      "Epoch 88/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6059 - accuracy: 0.7520 - val_loss: 0.8713 - val_accuracy: 0.6366\n",
      "\n",
      "Epoch 00088: val_accuracy did not improve from 0.63759\n",
      "Epoch 89/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6059 - accuracy: 0.7522 - val_loss: 0.8709 - val_accuracy: 0.6375\n",
      "\n",
      "Epoch 00089: val_accuracy did not improve from 0.63759\n",
      "Epoch 90/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6058 - accuracy: 0.7520 - val_loss: 0.8721 - val_accuracy: 0.6364\n",
      "\n",
      "Epoch 00090: val_accuracy did not improve from 0.63759\n",
      "Epoch 91/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6058 - accuracy: 0.7520 - val_loss: 0.8701 - val_accuracy: 0.6348\n",
      "\n",
      "Epoch 00091: val_accuracy did not improve from 0.63759\n",
      "Epoch 92/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6058 - accuracy: 0.7520 - val_loss: 0.8706 - val_accuracy: 0.6354\n",
      "\n",
      "Epoch 00092: val_accuracy did not improve from 0.63759\n",
      "Epoch 93/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6058 - accuracy: 0.7522 - val_loss: 0.8721 - val_accuracy: 0.6371\n",
      "\n",
      "Epoch 00093: val_accuracy did not improve from 0.63759\n",
      "Epoch 94/100\n",
      "560574/560574 [==============================] - 39s 70us/step - loss: 0.6059 - accuracy: 0.7521 - val_loss: 0.8699 - val_accuracy: 0.6366\n",
      "\n",
      "Epoch 00094: val_accuracy did not improve from 0.63759\n",
      "Epoch 95/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6057 - accuracy: 0.7520 - val_loss: 0.8705 - val_accuracy: 0.6357\n",
      "\n",
      "Epoch 00095: val_accuracy did not improve from 0.63759\n",
      "Epoch 96/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6057 - accuracy: 0.7525 - val_loss: 0.8699 - val_accuracy: 0.6356\n",
      "\n",
      "Epoch 00096: val_accuracy did not improve from 0.63759\n",
      "Epoch 97/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6057 - accuracy: 0.7524 - val_loss: 0.8743 - val_accuracy: 0.6339\n",
      "\n",
      "Epoch 00097: val_accuracy did not improve from 0.63759\n",
      "Epoch 98/100\n",
      "560574/560574 [==============================] - 40s 72us/step - loss: 0.6057 - accuracy: 0.7523 - val_loss: 0.8677 - val_accuracy: 0.6366\n",
      "\n",
      "Epoch 00098: val_accuracy did not improve from 0.63759\n",
      "Epoch 99/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6055 - accuracy: 0.7525 - val_loss: 0.8713 - val_accuracy: 0.6365\n",
      "\n",
      "Epoch 00099: val_accuracy did not improve from 0.63759\n",
      "Epoch 100/100\n",
      "560574/560574 [==============================] - 40s 71us/step - loss: 0.6056 - accuracy: 0.7522 - val_loss: 0.8708 - val_accuracy: 0.6362\n",
      "\n",
      "Epoch 00100: val_accuracy did not improve from 0.63759\n",
      "Performance on the train sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.7532921612490054\n",
      "\n",
      "\n",
      "Performance on the test sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.637588462402121\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_threshold = 10\n",
    "features = ['political', 'subscriptions']\n",
    "Number_of_components = 100\n",
    "clf_type='NN'\n",
    "\n",
    "model_fit_test(clf_type, features, bottom_threshold, Number_of_components, ['1', '2', '3', '4'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction performance (of LG) as a function of the anomaly threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['political', 'subscriptions']\n",
    "Number_of_components = 100\n",
    "clf_type='LG'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance on the train sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.6346561958008812\n",
      "\n",
      "\n",
      "Performance on the test sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.526568172870828\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_threshold = -1\n",
    "\n",
    "model_fit_test(clf_type, features, bottom_threshold, Number_of_components, ['1', '2', '3', '4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance on the train sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.7348344453754925\n",
      "\n",
      "\n",
      "Performance on the test sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.5951827981278986\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_threshold = 0\n",
    "\n",
    "model_fit_test(clf_type, features, bottom_threshold, Number_of_components, ['1', '2', '3', '4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance on the train sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.7356460213073325\n",
      "\n",
      "\n",
      "Performance on the test sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.6050068151488985\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_threshold = 5\n",
    "\n",
    "model_fit_test(clf_type, features, bottom_threshold, Number_of_components, ['1', '2', '3', '4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance on the train sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.7301622979303357\n",
      "\n",
      "\n",
      "Performance on the test sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.613836714884863\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_threshold = 10\n",
    "\n",
    "model_fit_test(clf_type, features, bottom_threshold, Number_of_components, ['1', '2', '3', '4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance on the train sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.7274514569269778\n",
      "\n",
      "\n",
      "Performance on the test sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.6120926464631586\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_threshold = 15\n",
    "\n",
    "model_fit_test(clf_type, features, bottom_threshold, Number_of_components, ['1', '2', '3', '4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance on the train sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.723399308387554\n",
      "\n",
      "\n",
      "Performance on the test sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.616331607459969\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_threshold = 20\n",
    "\n",
    "model_fit_test(clf_type, features, bottom_threshold, Number_of_components, ['1', '2', '3', '4'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction performance (of LG) as a function of features' combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "bottom_threshold = 10\n",
    "Number_of_components = 100\n",
    "clf_type='LG'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance on the train sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.7293292232604438\n",
      "\n",
      "\n",
      "Performance on the test sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.6128733167696464\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['political', 'subscriptions', 'age', 'sex']\n",
    "\n",
    "model_fit_test(clf_type, features, bottom_threshold, Number_of_components, ['1', '2', '3', '4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance on the train sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.7301640818161385\n",
      "\n",
      "\n",
      "Performance on the test sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.6138294712900118\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['political', 'subscriptions']\n",
    "\n",
    "model_fit_test(clf_type, features, bottom_threshold, Number_of_components, ['1', '2', '3', '4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance on the train sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.7281643458312372\n",
      "\n",
      "\n",
      "Performance on the test sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.6110624180568333\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['subscriptions']\n",
    "\n",
    "model_fit_test(clf_type, features, bottom_threshold, Number_of_components, ['1', '2', '3', '4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance on the train sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.2738529985241681\n",
      "\n",
      "\n",
      "Performance on the test sample:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.27305235991426446\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['political']\n",
    "\n",
    "model_fit_test(clf_type, features, bottom_threshold, Number_of_components, ['1', '2', '3', '4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
